Spatially constrained random walk approach for accurate estimation of airway wall surfaces
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 16th International Conference, Proceedings, Part II. Lecture Notes in Computer Science, vol. 8150, pp. 559–566.
Abstract
Assessing airway wall surfaces and the lumen from high resolution computed tomography (CT) scans are of great importance for diagnosing pulmonary diseases. However, accurately determining inner and outer airway wall surfaces of a complete 3-D tree structure can be quite challenging because of its complex nature. In this paper, we introduce a computational framework to accurately quantify airways through (i) a precise segmentation of the lumen, and (ii) a spatially constrained Markov random walk method to estimate the airway walls. Our results demonstrate that the proposed airway analysis platform identified the inner and outer airway surfaces better than methods commonly used in clinics, such as full width at half maximum and phase congruency.
Abstract sourceThis 2013 MICCAI paper presents a computational framework for airway lumen segmentation and airway wall surface estimation in computed tomography. The authors evaluated its wall estimates against expert annotations and compared them with commonly used methods.
Summary
The paper describes a framework for analyzing airways in computed tomography (CT) scans. It combines fuzzy-connectivity-based segmentation of the airway lumen with a spatially constrained Markov random walk method to estimate the inner and outer airway wall surfaces. The authors evaluated wall estimates on 300 resampled two-dimensional images using annotations from two independent experts, and compared results with full width at half maximum and phase congruency methods. The reported evaluation is retrospective; it does not establish clinical readiness.
Cite this work
Ziyue Xu; Ulas Bagci; Brent Foster; Awais Mansoor; Daniel J. Mollura (2013). Spatially constrained random walk approach for accurate estimation of airway wall surfaces. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 16th International Conference, Proceedings, Part II. Lecture Notes in Computer Science, vol. 8150, pp. 559–566. https://doi.org/10.1007/978-3-642-40763-5_69
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